Case Studies

How Mecklenburg County’s PRR Efficiency Sky-Rocketed with the Help of Exterro AI Technology

With the introduction of new technology over the last several years, Public Records Requests (PRRs) by governmental agencies have taken on a new look. Twyla Hollingsworth, an attorney for Mecklenburg County‘s local government in North Carolina, knows this intimately. Twyla is assigned to respond to Mecklenburg’s public records requests and manages other types of requests for litigation. After coming across Exterro’s AI-driven Smart ECA technology, Twyla quickly determined that this was a tool that would allow for multiple use cases across the legal spectrum—and provide a highly-efficient solution to her most time-consuming problems.

Customer Profile

Mecklenburg County was formed in 1762 from the western part of Anson County. The Mecklenburg County Board is focused on improving the local community, with its programs and services primed to four key areas: Community Health & Safety; Effective & Efficient Government; Social, Educational & Economic Opportunity; and Sustainable Communities.

What Is Smart ECA?

Smart Early Case Assessment (ECA) is an AI-driven technology that helps to quickly identify potentially responsive data prior to performing a full collection. In other words, it allows you to sift through vast troves of data housed by an enterprise or government to find materials that are relevant to your collection prior to performing the actual collection. This means that in-house legal teams are able to save themselves significant time, money, and resources by zeroing in and targeting the specific materials that they’d like to gather.

By exposing contextual relationships between custodians and content, Smart ECA technology allows you to explore specific concepts and “clusters” of data to find what you’re looking for, which significantly reduces review time and increases organizational efficiency.

Mecklenburg’s Challenges Prior to Smart ECA 

Twyla, who has been producing the Public Records Requests for Mecklenburg County for about a year and a half, says the process prior to the launch of Smart ECA was neither clean nor efficient.

“When I had to perform a request, I had to depend on others in the department to either provide me the documents in paper format, or I would have to call IT and have them place emails, for instance, on my computer,” says Twyla. “I would then have to go through my computer to review, and if I had to redact something, I had to print it out, go through the process of using a Sharpie to redact it, make copies so that you can’t read it when you hold it up to the light, and then try to scan it back in.”

Twyla says that the process was also sometimes disparate depending on whether the information needed to be produced as a physical or digital copy. The nature of this process disconnected Twyla from IT, and she would be left waiting to determine how best to continue with the PRR process.

“I don’t know what happened on the backend [with IT] after I performed my redactions,” says Twyla, and that the process with the physical copies “was very time consuming.” “We would end up with boxes of papers.”

Uncovering A More Efficient Method

Once Mecklenburg County’s legal team discovered the power that Smart ECA could provide in answering PRRs from the local citizenry, they jumped on the opportunity to utilize the tool.

“Once I got an understanding of what [Smart ECA] does, I asked [others on the team] if this was something they’d be interested in exploring,” says Kam Gallman, the Senior IT Programmer & Analyst for Mecklenburg County. “The reason is [we] get a lot of PRRs, and I didn’t really know exactly how time consuming it is when public requests come in until Twyla had a problem once and explained it to me.”

Kam and Twyla worked together to determine how best to solve this issue. They got a Smart ECA demo from Exterro, which gave them an early indication of how the technology could increase their efficiency by drilling down massive amounts of personal data to show only what’s relevant.

“It appeared that it would be less time consuming, as Twyla wouldn’t have to dive through as many emails to get what she was looking for,” says Kam. “So I thought that it would be a lot more efficient, and that’s how it has turned out so far.”

Life After Smart ECA 

For Kam and Twyla, the benefits of using Smart ECA were clear from the start. Smart ECA analyzes keywords and makes suggestions automatically based on communication and context-based relationships between data sets. This means that Twyla is able to quickly and easily find the data she needs to complete these requests without doing any extra work, because these suggestions are served up automatically by the AI.

“It is extremely useful because the public records requests are coming from regular citizens who don’t use language that would make discovery easy, so in the past I would have to run the whole gamut in terms of what they’re looking for,” Twyla explains. “And sometimes they may ask for a huge amount of data—the whole kitchen sink. So if we’re able to drill down in certain terms what they’re looking for, especially with technology that automatically clusters keywords and concepts together, that’s extremely helpful.”

And for those requests that end up being very specific, the other aspects of Smart ECA—such as in-place document rendering to help determine whether it’s the correct data—Twyla sees the technology as a continual time-saver.

“[Sometimes] people are very specific—or at least they try to be—about what they’re looking for,” says Twyla. “The technology allows us to produce data in different ways, if we want to.”

“I Plan to Use It On Everything”

Overall, Kam Gallman and Twyla Hollingsworth see Smart ECA offering them multiple use cases for e-discovery, litigation, and custodian profiling along with their current use for PRRs. Twyla looks forward to having the ability to kick off a Smart ECA workflow from Exterro’s Project Management module as well.

“I would like to be able to use it on all of our requests,” says Twyla. “Based on our uses, I foresee that happening. If I’m able to deal with subsets of data and it’s effective for our current PRR uses, then I plan to use it on everything.”

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